{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-11-09T12:08:07.358510Z",
     "start_time": "2024-11-09T12:08:07.347974Z"
    }
   },
   "source": [
    "import os\n",
    "from matplotlib import pyplot\n",
    "from numpy import vstack\n",
    "from numpy import argmax\n",
    "from pandas import read_csv\n",
    "from sklearn.metrics import accuracy_score\n",
    "from torchvision.datasets import MNIST\n",
    "from torchvision.transforms import Compose, ToTensor, Normalize\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.nn import Conv2d, MaxPool2d, Linear, ReLU, Softmax, Module\n",
    "from torch.optim import SGD\n",
    "from torch.nn import CrossEntropyLoss\n",
    "from torch.nn.init import kaiming_uniform_, xavier_uniform_\n",
    "import torch\n",
    "\n",
    "# 检查是否有GPU可用\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "if torch.cuda.is_available():\n",
    "    print(torch.cuda.get_device_name(0),\"is available\")\n",
    "else:\n",
    "    print(\"GPU不可用，使用CPU\")\n",
    "\n",
    "\n",
    "\n",
    "# 模型定义\n",
    "class CNN(Module):\n",
    "    # 定义模型属性\n",
    "    def __init__(self, n_channels):\n",
    "        super(CNN, self).__init__()\n",
    "        self.hidden1 = Conv2d(n_channels, 32, (3, 3))\n",
    "        kaiming_uniform_(self.hidden1.weight, nonlinearity='relu')\n",
    "        self.act1 = ReLU()\n",
    "        self.pool1 = MaxPool2d((2, 2), stride=(2, 2))\n",
    "        self.hidden2 = Conv2d(32, 32, (3, 3))\n",
    "        kaiming_uniform_(self.hidden2.weight, nonlinearity='relu')\n",
    "        self.act2 = ReLU()\n",
    "        self.pool2 = MaxPool2d((2, 2), stride=(2, 2))\n",
    "        self.hidden3 = Linear(5 * 5 * 32, 100)\n",
    "        kaiming_uniform_(self.hidden3.weight, nonlinearity='relu')\n",
    "        self.act3 = ReLU()\n",
    "        self.hidden4 = Linear(100, 10)\n",
    "        xavier_uniform_(self.hidden4.weight)\n",
    "        self.act4 = Softmax(dim=1)\n",
    "\n",
    "        # 前向传播\n",
    "\n",
    "    def forward(self, X):\n",
    "        X = self.hidden1(X)\n",
    "        X = self.act1(X)\n",
    "        X = self.pool1(X)\n",
    "        X = self.hidden2(X)\n",
    "        X = self.act2(X)\n",
    "        X = self.pool2(X)\n",
    "        X = X.view(-1, 5 * 5 * 32)\n",
    "        X = self.hidden3(X)\n",
    "        X = self.act3(X)\n",
    "        X = self.hidden4(X)\n",
    "        X = self.act4(X)\n",
    "        return X\n",
    "\n",
    "# 准备数据集\n",
    "def prepare_data(path):\n",
    "    trans = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])\n",
    "    train = MNIST(path, train=True, download=True, transform=trans)\n",
    "    test = MNIST(path, train=False, download=True, transform=trans)\n",
    "    train_dl = DataLoader(train, batch_size=64, shuffle=True)\n",
    "    test_dl = DataLoader(test, batch_size=1024, shuffle=False)\n",
    "    i, (inputs, targets) = next(enumerate(train_dl))\n",
    "    for i in range(25):\n",
    "        pyplot.subplot(5, 5, i + 1)\n",
    "        pyplot.imshow(inputs[i][0], cmap='gray')\n",
    "    print(\"Data loading...\")\n",
    "    pyplot.show()\n",
    "    return train_dl, test_dl\n",
    "\n",
    "\n",
    "# 训练模型\n",
    "def train_model(train_dl, model):\n",
    "    criterion = CrossEntropyLoss()\n",
    "    optimizer = SGD(model.parameters(), lr=0.012, momentum=0.85)\n",
    "    for epoch in range(10):\n",
    "        for i, (inputs, targets) in enumerate(train_dl):\n",
    "            inputs, targets = inputs.to(device), targets.to(device)  # 将数据转移到GPU\n",
    "            optimizer.zero_grad()\n",
    "            yhat = model(inputs)\n",
    "            loss = criterion(yhat, targets)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            if i % 200 == 0:\n",
    "                print('Train Epoch: {} [{:6d}/{} ({:2.0f}%)]\\tLoss: {:2.6f}'.format(epoch,  i * len(inputs), len(train_dl.dataset),100. * i / len(train_dl), loss.item()))\n",
    "\n",
    "\n",
    "# 评估模型\n",
    "def evaluate_model(test_dl, model):\n",
    "    predictions, actuals = list(), list()\n",
    "    for i, (inputs, targets) in enumerate(test_dl):\n",
    "        inputs, targets = inputs.to(device), targets.to(device)  # 将数据转移到GPU\n",
    "        yhat = model(inputs)\n",
    "        yhat = yhat.detach().cpu().numpy()  # 将结果转移回CPU进行计算\n",
    "        actual = targets.cpu().numpy()\n",
    "        yhat = argmax(yhat, axis=1)\n",
    "        actual = actual.reshape((len(actual), 1))\n",
    "        yhat = yhat.reshape((len(yhat), 1))\n",
    "        predictions.append(yhat)\n",
    "        actuals.append(actual)\n",
    "\n",
    "    predictions, actuals = vstack(predictions), vstack(actuals)\n",
    "    acc = accuracy_score(actuals, predictions)\n",
    "    return acc\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NVIDIA GeForce RTX 3060 Laptop GPU is available\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-09T12:08:07.997562Z",
     "start_time": "2024-11-09T12:08:07.358510Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 准备数据\n",
    "path = os.path.join(os.getcwd(), 'datasets/mnist')\n",
    "train_dl, test_dl = prepare_data(path)\n",
    "print(\"Number of training samples:\", len(train_dl.dataset))\n",
    "print(\"Number of test samples:\", len(test_dl.dataset))"
   ],
   "id": "9ea514f731de6f8d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loading...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 25 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training samples: 60000\n",
      "Number of test samples: 10000\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-09T12:08:08.012816Z",
     "start_time": "2024-11-09T12:08:08.005881Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 定义网络并移动到GPU\n",
    "model = CNN(1).to(device)\n",
    "print(\"CNN Network Definition Complete\")"
   ],
   "id": "acd51d1650c95c0b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN Network Definition Complete\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-09T12:09:04.176131Z",
     "start_time": "2024-11-09T12:08:08.017829Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 训练模型\n",
    "print(\"Model training...\")\n",
    "train_model(train_dl, model)\n",
    "print(\"CNN Network Training Complete\")"
   ],
   "id": "19809ec669111acf",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model training...\n",
      "Train Epoch: 0 [     0/60000 (0%)]\tLoss: 2.322292\n",
      "Train Epoch: 0 [ 12800/60000 (21%)]\tLoss: 1.706031\n",
      "Train Epoch: 0 [ 25600/60000 (43%)]\tLoss: 1.693283\n",
      "Train Epoch: 0 [ 38400/60000 (64%)]\tLoss: 1.655425\n",
      "Train Epoch: 0 [ 51200/60000 (85%)]\tLoss: 1.704165\n",
      "Train Epoch: 1 [     0/60000 (0%)]\tLoss: 1.638590\n",
      "Train Epoch: 1 [ 12800/60000 (21%)]\tLoss: 1.638000\n",
      "Train Epoch: 1 [ 25600/60000 (43%)]\tLoss: 1.595530\n",
      "Train Epoch: 1 [ 38400/60000 (64%)]\tLoss: 1.646472\n",
      "Train Epoch: 1 [ 51200/60000 (85%)]\tLoss: 1.640022\n",
      "Train Epoch: 2 [     0/60000 (0%)]\tLoss: 1.599574\n",
      "Train Epoch: 2 [ 12800/60000 (21%)]\tLoss: 1.582917\n",
      "Train Epoch: 2 [ 25600/60000 (43%)]\tLoss: 1.540277\n",
      "Train Epoch: 2 [ 38400/60000 (64%)]\tLoss: 1.570287\n",
      "Train Epoch: 2 [ 51200/60000 (85%)]\tLoss: 1.524585\n",
      "Train Epoch: 3 [     0/60000 (0%)]\tLoss: 1.554489\n",
      "Train Epoch: 3 [ 12800/60000 (21%)]\tLoss: 1.533216\n",
      "Train Epoch: 3 [ 25600/60000 (43%)]\tLoss: 1.525884\n",
      "Train Epoch: 3 [ 38400/60000 (64%)]\tLoss: 1.580260\n",
      "Train Epoch: 3 [ 51200/60000 (85%)]\tLoss: 1.507463\n",
      "Train Epoch: 4 [     0/60000 (0%)]\tLoss: 1.659109\n",
      "Train Epoch: 4 [ 12800/60000 (21%)]\tLoss: 1.535370\n",
      "Train Epoch: 4 [ 25600/60000 (43%)]\tLoss: 1.558871\n",
      "Train Epoch: 4 [ 38400/60000 (64%)]\tLoss: 1.523520\n",
      "Train Epoch: 4 [ 51200/60000 (85%)]\tLoss: 1.553126\n",
      "Train Epoch: 5 [     0/60000 (0%)]\tLoss: 1.526935\n",
      "Train Epoch: 5 [ 12800/60000 (21%)]\tLoss: 1.495015\n",
      "Train Epoch: 5 [ 25600/60000 (43%)]\tLoss: 1.646837\n",
      "Train Epoch: 5 [ 38400/60000 (64%)]\tLoss: 1.586903\n",
      "Train Epoch: 5 [ 51200/60000 (85%)]\tLoss: 1.599376\n",
      "Train Epoch: 6 [     0/60000 (0%)]\tLoss: 1.537591\n",
      "Train Epoch: 6 [ 12800/60000 (21%)]\tLoss: 1.604519\n",
      "Train Epoch: 6 [ 25600/60000 (43%)]\tLoss: 1.554779\n",
      "Train Epoch: 6 [ 38400/60000 (64%)]\tLoss: 1.575269\n",
      "Train Epoch: 6 [ 51200/60000 (85%)]\tLoss: 1.642950\n",
      "Train Epoch: 7 [     0/60000 (0%)]\tLoss: 1.554634\n",
      "Train Epoch: 7 [ 12800/60000 (21%)]\tLoss: 1.628999\n",
      "Train Epoch: 7 [ 25600/60000 (43%)]\tLoss: 1.577384\n",
      "Train Epoch: 7 [ 38400/60000 (64%)]\tLoss: 1.626665\n",
      "Train Epoch: 7 [ 51200/60000 (85%)]\tLoss: 1.569972\n",
      "Train Epoch: 8 [     0/60000 (0%)]\tLoss: 1.588701\n",
      "Train Epoch: 8 [ 12800/60000 (21%)]\tLoss: 1.521380\n",
      "Train Epoch: 8 [ 25600/60000 (43%)]\tLoss: 1.508066\n",
      "Train Epoch: 8 [ 38400/60000 (64%)]\tLoss: 1.482750\n",
      "Train Epoch: 8 [ 51200/60000 (85%)]\tLoss: 1.482745\n",
      "Train Epoch: 9 [     0/60000 (0%)]\tLoss: 1.462252\n",
      "Train Epoch: 9 [ 12800/60000 (21%)]\tLoss: 1.481723\n",
      "Train Epoch: 9 [ 25600/60000 (43%)]\tLoss: 1.465080\n",
      "Train Epoch: 9 [ 38400/60000 (64%)]\tLoss: 1.482340\n",
      "Train Epoch: 9 [ 51200/60000 (85%)]\tLoss: 1.477745\n",
      "CNN Network Training Complete\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-09T12:09:04.879601Z",
     "start_time": "2024-11-09T12:09:04.182841Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 评估模型\n",
    "acc = evaluate_model(test_dl, model)\n",
    "print('CNN Network accuracy: %.3f' % acc)"
   ],
   "id": "e08be11bdc0a7aff",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN Network accuracy: 0.986\n"
     ]
    }
   ],
   "execution_count": 15
  }
 ],
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